SkillsBench Brings Gym-Style Evaluation to Agent Development

A new open-source benchmark treats AI agent evaluation like RL training — discrete, reproducible skill tests in controlled environments.

The agent evaluation gap — knowing whether your agent actually works before deploying it — just got a new tool. @tom_doerr highlighted SkillsBench, an open-source framework on GitHub that applies the "gym" paradigm from reinforcement learning to AI agent assessment. Rather than testing agents on broad, fuzzy tasks, SkillsBench defines discrete skills and evaluates them individually in controlled environments.

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